Instructions to use nhmnhat1997/condenser-phobert-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nhmnhat1997/condenser-phobert-biencoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nhmnhat1997/condenser-phobert-biencoder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use nhmnhat1997/condenser-phobert-biencoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nhmnhat1997/condenser-phobert-biencoder") model = AutoModel.from_pretrained("nhmnhat1997/condenser-phobert-biencoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a43360f65ec6e500c654716121d7dafff641440786950dee20a24657e00cb33
- Size of remote file:
- 540 MB
- SHA256:
- 3757dcf868f10b68cf148e4e12a05084e1133e7b6a738a5acf1c3165714f301b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.